Evolution strategies for multivariate-to-anything partially specified random vector generation

Multivariate-to-anything methods for partially specified random vector generation work by transforming samples from a driving distribution into samples characterized by given marginals and correlations. The correlations of the transformed random vector are controlled by the driving distribution; sam...

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Bibliographic Details
Main Author: STANHOPE, Stephen
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Character generation
Computer science
control theory
systems
Exact sciences and technology
Gaussian distribution
Gradient methods
Pairwise error probability
Random number generation
Random variables
Sampling methods
Statistical distributions
Stochastic processes
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Summary:Multivariate-to-anything methods for partially specified random vector generation work by transforming samples from a driving distribution into samples characterized by given marginals and correlations. The correlations of the transformed random vector are controlled by the driving distribution; sampling a partially specified random vector requires finding an appropriate driving distribution. This paper motivates the use of evolution strategies for solving such problems and compares evolution strategies to conjugate gradient methods in the context of solving a Dirichlet-to-anything transformation. It is shown that the evolution strategy is at least as effective as the conjugate gradient method for solution of the parameterization problem.
DOI:10.1109/CEC.2004.1331175